AI & Software

Most software bugs are never reported. They're silently absorbed by users who just stop doing the thing that broke. That silence is costing you more than you think.

When you send a message to an AI model, it gets transformed before the model ever sees it. Here's what actually happens in that gap.

Every developer knows stateless systems are easier to reason about. Yet every system drifts toward stateful complexity. Here's why that happens, and what it costs you.

When you ask an LLM to summarize a document, it isn't reading and condensing. It's doing something stranger and more limited than that.

We obsess over algorithmic complexity and cache efficiency while ignoring the most powerful optimization available: not executing the work at all.

A look at what actually happens inside 'chain-of-thought' prompting, and why the answer matters for how you use these tools.

AI coding assistants generate plausible, passing code. That's exactly why they're dangerous. The fix and the solution are not the same thing.

The person who knows how to talk to your AI model may matter more than the person who built it. Here's why that shift is already happening.

Bugs that vanish under observation aren't mysterious. They're symptoms of systems that depend on timing, state, or environment in ways you haven't accounted for.

A 100,000-token context window sounds like perfect memory. It isn't. Here's what's actually happening inside that attention mechanism.

Adding features is celebrated. Removing them is where real engineering judgment lives, and most teams are terrible at it.

Git and its cousins do store your work, but that's the least interesting thing they do. Here's what version control is actually for.

A software team spent weeks feeding their model richer data and watched accuracy drop. Here's what went wrong and what it reveals about how LLMs actually process information.

Training your model is the starting line, not the finish. Here's what actually happens to model performance over time, and how to manage it.

Heisenbugs don't just frustrate developers. They expose a fundamental flaw in how we think about software correctness.

LLMs don't know when they're wrong. Understanding the three ways they fail silently is the skill that separates confident AI users from the ones who get burned.

Before transformers, before LLMs, before vector databases, there was one foundational move: turning meaning into math. Here's how it actually works.

A suppressed warning in Toyota's embedded software contributed to one of the costliest automotive recalls in history. The warning was real. The team just stopped looking.